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Record W3111846458 · doi:10.1002/srin.202000523

Effect of Modified Water‐Bath Method on Microstructure and Mechanical Properties of Wire Arc Additive Manufactured Low‐Carbon Low‐Alloy Steel

2020· article· en· W3111846458 on OpenAlexaff
Jingchuan Luo, Guoqiang You, Dashi Lu, Sheng Zeng, Lizhen Peng, Qing Liu

Bibliographic record

Venuesteel research international · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsMD Precision (Canada)
FundersNatural Science Foundation of Chongqing
KeywordsMicrostructureMaterials scienceIndentation hardnessBainiteFerrite (magnet)MetallurgyEquiaxed crystalsAlloyUltimate tensile strengthComposite materialLathMartensite

Abstract

fetched live from OpenAlex

Of late, wire arc additive manufacturing (WAAM) is extensively used in the aerospace and automotive fields to produce large complex metallic components. The water‐bath method is applied for active cooling to address the heat accumulation problem in WAAM. Herein, the modified water‐bath method with a changing level is used to realize different phase transformations, and the microstructure and mechanical properties of the sample are investigated. Heat accumulation in the sample is eliminated using the modified water‐bath method. Furthermore, the microstructure of the fabricated sample shows a mixture of polygonal ferrite (PF), upper bainite (UB), and lath bainite (LB). Layer bands are formed in the entire sample, except in the final layer, and equiaxed ferrite (EF) and an increased fraction of PF appear in these zones. The microhardness and tensile property are enhanced due to the fine LB. The considerable difference in the microhardness between LB and PF causes an obvious wave of hardness, and the tensile property along the horizontal direction is better than the vertical. This study is expected to help broaden the application of the water bath in the microstructure control.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.030
GPT teacher head0.300
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2020
Admission routes1
Has abstractyes

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